\(\bullet \)
ARIMA [1]}: A traditional autoregressive integrated moving average method for time series prediction and often used as a baseline.

\(\bullet \)
AE-LSTM [2]}: An LSTM network that uses an autoencoder for deep feature extraction and provides a deterministic prediction.

\(\bullet \)
SARIMAX [3]}: An autoregressive model that can handle seasonality and exogenous features of time series.

\(\bullet \)
UberNN [4]}: An LSTM-based model that uses Monte Carlo dropout to provide uncertainty and is able to extract deep features of time series through autoencoders.

\(\bullet \)
TSE-SC [5]}: was recently proposed as a Transformer-based Deep Learning model that can forecast abrupt changes accurately. (i) STAR Decomposition to automatically ex-
tract essential features such as anomalies, (ii) an Anomaly-Aware Model to leverage such
extracted features, and (iii) a Dynamic Uncertainty Optimization to reduce the uncertainty of the network. The final predicted

\(\bullet \)
AA-Forecast (LSTM) is our proposed model with LSTM cells.

\(\bullet \)
AA-Forecast (GRU) is our proposed model with GRU cells.


